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When Large Language Models Meet Vector Databases: A Survey

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arxiv 2402.01763 v4 pith:4UIM3NAW submitted 2024-01-30 cs.DB cs.AIcs.CLcs.LG

Zhi Jing , Yongye Su , Yikun Han , Bo Yuan , Haiyun Xu , Chunjiang Liu , Kehai Chen , Min Zhang This is my paper
classification cs.DBcs.AIcs.CLcs.LG
keywords llmsvecdbsvectordatabasesissuesknowledgelanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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This survey explores the synergistic potential of Large Language Models (LLMs) and Vector Databases (VecDBs), a burgeoning but rapidly evolving research area. With the proliferation of LLMs comes a host of challenges, including hallucinations, outdated knowledge, prohibitive commercial application costs, and memory issues. VecDBs emerge as a compelling solution to these issues by offering an efficient means to store, retrieve, and manage the high-dimensional vector representations intrinsic to LLM operations. Through this nuanced review, we delineate the foundational principles of LLMs and VecDBs and critically analyze their integration's impact on enhancing LLM functionalities. This discourse extends into a discussion on the speculative future developments in this domain, aiming to catalyze further research into optimizing the confluence of LLMs and VecDBs for advanced data handling and knowledge extraction capabilities.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search

    cs.DB 2025-05 reject novelty 6.0 of 10

    DARTH learns to predict a query's current recall during HNSW/IVF search and stops early at a user-specified target, achieving speedups up to 14.6x on HNSW and 41.8x on IVF, yet 13-15% of queries miss the target.

  2. HENN: A Hierarchical Epsilon Net Navigation Graph for Approximate Nearest Neighbor Search

    cs.DS 2025-05 reject novelty 6.0 of 10

    HENN constructs hierarchical nearest-neighbor graphs with epsilon-net layers, claiming polylogarithmic query time and showing speedups over HNSW on skewed data.

  3. Hallucination Detection with Small Language Models

    cs.CL 2025-06 reject novelty 5.0 of 10

    A multi-small-model ensemble with sentence splitting, z-score normalization, and harmonic mean detects hallucinations in RAG answers with a reported 10% F1 gain over single-model baselines.

  4. TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows

    cs.DB 2025-06 reject novelty 4.0 of 10

    TableVault describes a system design for managing versioned, reproducible dataframe collections in LLM-augmented workflows, but it ships no implementation or evaluation.

  5. Provably Secure Retrieval-Augmented Generation

    cs.CR 2025-08 reject novelty 2.0 of 10

    SAG encrypts RAG knowledge bases and claims formal security, but its proofs are flawed and its benchmarks guarantee zero attack success by design.

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